News-Based Indices on Country Fundamentals

Andras Fulop, Z. Kocsis
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Abstract

We propose a novel method to extract information on macro fundamentals based on regular expressions and compare its performance vis-a-vis two popular alternatives in the current finance literature based on predefined dictionaries and supervised learning techniques. We create news indices about fundamentals according to these techniques on a news dataset by Reuters and compare how the techniques fare at (i) capturing observed economic surprises (macro announcements compared to Bloomberg survey expectations) and (ii) correctly predicting labels on a manually classified test sample of the news text corpus. We demonstrate that our methodology is better able to identify and discriminate among fundamentals than the alternative techniques. We further show the benefit of our fundamental news indices in an econometric application that investigates the fundamental content of asset prices in the sovereign credit risk arena.
基于新闻的国家基本面指数
我们提出了一种基于正则表达式提取宏观基本面信息的新方法,并将其与当前金融文献中基于预定义字典和监督学习技术的两种流行替代方法进行了性能比较。我们根据这些技术在路透社的新闻数据集上创建了关于基本面的新闻指数,并比较了这些技术在以下方面的表现:(i)捕捉观察到的经济意外(与彭博调查预期相比的宏观公告)和(ii)正确预测新闻文本语料库手动分类测试样本上的标签。我们证明,我们的方法能够更好地识别和区分基本面比替代技术。我们进一步在计量经济学应用中展示了我们的基本新闻指数的好处,该应用调查了主权信用风险领域资产价格的基本内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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